{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸识别与计算机视觉文档实践\n",
    "\n",
    ">* 本周主要内容：人脸（Face） API文档不同平台对比与实践及计算机视觉入门（认知服务）\n",
    ">* 202009_API_人工智能与机器学习_week03\n",
    ">*  电子讲义设计者：许智超\n",
    "<br/>\n",
    "<br/>\n",
    "\n",
    "## 复习\n",
    "\n",
    "* 上周主要内容： \n",
    ">    * 1、API文档\n",
    ">    * 2、认知服务-人脸识别\n",
    ">    * 3、pandas黑魔法（json_normalize）\n",
    "-----\n",
    "* 提问、检查与扩展：\n",
    ">    * 1、上周的Azure face API随机检查2位同学，对于API文档是否有能力详细阅读？\n",
    ">    * 2、抽查2位同学，对于获取的数据用途有何思考？是否尝试其他平台（XXX API、XXX API）      \n",
    ">    * 3、扩展: 我们尝试抽取了4张图片，包含AB、ABC、BCD、ABCD四个人物特征，如何通过API检查数据差异？\n",
    "  \n",
    "\n",
    "-----\n",
    "## 本周学习目标：\n",
    "\n",
    "> $\\mathcal{1、Azure 认知服务-人脸集合演示}$     \n",
    "> $\\mathcal{2、Face++ 之 FaceSets 实践}$     \n",
    "> $\\mathcal{3、计算机视觉实践}$    \n",
    "\n",
    "\n",
    "\n",
    "## 学生权限\n",
    "\n",
    "* [访问学生权益](https://azure.microsoft.com/zh-cn/education/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "/* 本电子讲义使用之CSS */\n",
       "div.code_cell {\n",
       "    background-color: #e5f1fe;\n",
       "}\n",
       "div.cell.selected {\n",
       "    background-color: #effee2;\n",
       "    font-size: 2rem;\n",
       "    line-height: 2.4rem;\n",
       "}\n",
       "div.cell.selected .rendered_html table {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html pre code {\n",
       "    background-color: #C4E4ff;   \n",
       "    padding: 2px 25px;\n",
       "}\n",
       ".rendered_html pre {\n",
       "    background-color: #99c9ff;\n",
       "}\n",
       "div.code_cell .CodeMirror {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html img, .rendered_html svg {\n",
       "    max-width: 50%;\n",
       "    height: auto;\n",
       "    float: center;\n",
       "}\n",
       "/* Gradient transparent - color - transparent */\n",
       "hr {\n",
       "    border: 0;\n",
       "    border-bottom: 1px dashed #ccc;\n",
       "}\n",
       ".emoticon{\n",
       "    font-size: 5rem;\n",
       "    line-height: 4.4rem;\n",
       "    text-align: center;\n",
       "    vertical-align: middle;\n",
       "}\n",
       "\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style>\n",
    "/* 本电子讲义使用之CSS */\n",
    "div.code_cell {\n",
    "    background-color: #e5f1fe;\n",
    "}\n",
    "div.cell.selected {\n",
    "    background-color: #effee2;\n",
    "    font-size: 2rem;\n",
    "    line-height: 2.4rem;\n",
    "}\n",
    "div.cell.selected .rendered_html table {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html pre code {\n",
    "    background-color: #C4E4ff;   \n",
    "    padding: 2px 25px;\n",
    "}\n",
    ".rendered_html pre {\n",
    "    background-color: #99c9ff;\n",
    "}\n",
    "div.code_cell .CodeMirror {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html img, .rendered_html svg {\n",
    "    max-width: 50%;\n",
    "    height: auto;\n",
    "    float: center;\n",
    "}\n",
    "/* Gradient transparent - color - transparent */\n",
    "hr {\n",
    "    border: 0;\n",
    "    border-bottom: 1px dashed #ccc;\n",
    "}\n",
    ".emoticon{\n",
    "    font-size: 5rem;\n",
    "    line-height: 4.4rem;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    "\n",
    "</style>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 复习\n",
    "\n",
    "> 1、回顾阅读API文档的关键点     \n",
    "> 2、正确阅读json数据 [jsonviewer.stack.hu(json转换检查数据)](http://jsonviewer.stack.hu)    \n",
    "> 3、pandas 中的json_normalize模块/函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-1 面部检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"faceId\": \"218551de-53ca-401a-8209-7c315c98018d\", \"faceRectangle\": {\"top\": 297, \"left\": 142, \"width\": 557, \"height\": 557}, \"faceAttributes\": {\"smile\": 0.0, \"headPose\": {\"pitch\": -2.0, \"roll\": 13.4, \"yaw\": -16.6}, \"gender\": \"male\", \"age\": 19.0, \"facialHair\": {\"moustache\": 0.1, \"beard\": 0.1, \"sideburns\": 0.1}, \"glasses\": \"NoGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.0, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 0.0, \"neutral\": 1.0, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.16}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.61}, \"noise\": {\"noiseLevel\": \"medium\", \"value\": 0.48}, \"makeup\": {\"eyeMakeup\": true, \"lipMakeup\": true}, \"accessories\": [], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.1, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 1.0}, {\"color\": \"other\", \"confidence\": 0.58}, {\"color\": \"brown\", \"confidence\": 0.54}, {\"color\": \"gray\", \"confidence\": 0.41}, {\"color\": \"blond\", \"confidence\": 0.04}, {\"color\": \"red\", \"confidence\": 0.02}, {\"color\": \"white\", \"confidence\": 0.0}]}}}]'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-1 面部检测\n",
    "import requests\n",
    "import json\n",
    "\n",
    "# set to your own subscription key value\n",
    "subscription_key = \"d6d151266e1448de9aa63beaa30edd99\"\n",
    "assert subscription_key\n",
    "\n",
    "# replace <My Endpoint String> with the string from your endpoint URL\n",
    "face_api_url = 'https://api-wy.cognitiveservices.azure.com//face/v1.0/detect'\n",
    "\n",
    "# 请求正文body\n",
    "image_url = 'http://img.mp.itc.cn/upload/20170520/a0b0c6c6f52343d2af23689ddca17f66_th.jpg'\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "\n",
    "# 请求参数parameters\n",
    "params = {\n",
    "    'returnFaceId': 'true',\n",
    "    'returnFaceLandmarks': 'false',\n",
    "    # 可选参数,请仔细阅读API文档\n",
    "    'returnFaceAttributes': 'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise',\n",
    "}\n",
    "\n",
    "response = requests.post(face_api_url, params=params,\n",
    "                         headers=headers, json={\"url\": image_url})\n",
    "# json.dumps 将json--->bytes\n",
    "json.dumps(response.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-2 json转译\n",
    "\n",
    "> * bytes ---> json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'faceId': 'c9c0aaf0-534c-413b-a243-feb3c8c483b7',\n",
       "  'faceRectangle': {'top': 215, 'left': 239, 'width': 227, 'height': 227},\n",
       "  'faceAttributes': {'smile': 1.0,\n",
       "   'headPose': {'pitch': -12.3, 'roll': 4.7, 'yaw': -4.1},\n",
       "   'gender': 'male',\n",
       "   'age': 22.0,\n",
       "   'facialHair': {'moustache': 0.1, 'beard': 0.1, 'sideburns': 0.1},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.0,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 1.0,\n",
       "    'neutral': 0.0,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'medium', 'value': 0.68},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.54},\n",
       "   'noise': {'noiseLevel': 'low', 'value': 0.14},\n",
       "   'makeup': {'eyeMakeup': True, 'lipMakeup': True},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.05,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'brown', 'confidence': 1.0},\n",
       "     {'color': 'black', 'confidence': 0.63},\n",
       "     {'color': 'red', 'confidence': 0.59},\n",
       "     {'color': 'blond', 'confidence': 0.18},\n",
       "     {'color': 'other', 'confidence': 0.04},\n",
       "     {'color': 'gray', 'confidence': 0.03},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-2\n",
    "results = response.json()\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-3 pandas 数据表格化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faceId</th>\n",
       "      <th>faceRectangle.top</th>\n",
       "      <th>faceRectangle.left</th>\n",
       "      <th>faceRectangle.width</th>\n",
       "      <th>faceRectangle.height</th>\n",
       "      <th>faceAttributes.smile</th>\n",
       "      <th>faceAttributes.headPose.pitch</th>\n",
       "      <th>faceAttributes.headPose.roll</th>\n",
       "      <th>faceAttributes.headPose.yaw</th>\n",
       "      <th>faceAttributes.gender</th>\n",
       "      <th>...</th>\n",
       "      <th>faceAttributes.noise.value</th>\n",
       "      <th>faceAttributes.makeup.eyeMakeup</th>\n",
       "      <th>faceAttributes.makeup.lipMakeup</th>\n",
       "      <th>faceAttributes.accessories</th>\n",
       "      <th>faceAttributes.occlusion.foreheadOccluded</th>\n",
       "      <th>faceAttributes.occlusion.eyeOccluded</th>\n",
       "      <th>faceAttributes.occlusion.mouthOccluded</th>\n",
       "      <th>faceAttributes.hair.bald</th>\n",
       "      <th>faceAttributes.hair.invisible</th>\n",
       "      <th>faceAttributes.hair.hairColor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c9c0aaf0-534c-413b-a243-feb3c8c483b7</td>\n",
       "      <td>215</td>\n",
       "      <td>239</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-12.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>male</td>\n",
       "      <td>...</td>\n",
       "      <td>0.14</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'brown', 'confidence': 1.0}, {'colo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 faceId  faceRectangle.top  \\\n",
       "0  c9c0aaf0-534c-413b-a243-feb3c8c483b7                215   \n",
       "\n",
       "   faceRectangle.left  faceRectangle.width  faceRectangle.height  \\\n",
       "0                 239                  227                   227   \n",
       "\n",
       "   faceAttributes.smile  faceAttributes.headPose.pitch  \\\n",
       "0                   1.0                          -12.3   \n",
       "\n",
       "   faceAttributes.headPose.roll  faceAttributes.headPose.yaw  \\\n",
       "0                           4.7                         -4.1   \n",
       "\n",
       "  faceAttributes.gender  ...  faceAttributes.noise.value  \\\n",
       "0                  male  ...                        0.14   \n",
       "\n",
       "   faceAttributes.makeup.eyeMakeup  faceAttributes.makeup.lipMakeup  \\\n",
       "0                             True                             True   \n",
       "\n",
       "   faceAttributes.accessories faceAttributes.occlusion.foreheadOccluded  \\\n",
       "0                          []                                     False   \n",
       "\n",
       "   faceAttributes.occlusion.eyeOccluded  \\\n",
       "0                                 False   \n",
       "\n",
       "   faceAttributes.occlusion.mouthOccluded  faceAttributes.hair.bald  \\\n",
       "0                                   False                      0.05   \n",
       "\n",
       "   faceAttributes.hair.invisible  \\\n",
       "0                          False   \n",
       "\n",
       "                       faceAttributes.hair.hairColor  \n",
       "0  [{'color': 'brown', 'confidence': 1.0}, {'colo...  \n",
       "\n",
       "[1 rows x 38 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-3\n",
    "import pandas as pd\n",
    "df_face = pd.json_normalize(results)\n",
    "df_face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-4 数据取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['c9c0aaf0-534c-413b-a243-feb3c8c483b7']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceID = df_face['faceId'].values.tolist()\n",
    "faceID "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['faceId', 'faceRectangle.top', 'faceRectangle.left',\n",
       "       'faceRectangle.width', 'faceRectangle.height', 'faceAttributes.smile',\n",
       "       'faceAttributes.headPose.pitch', 'faceAttributes.headPose.roll',\n",
       "       'faceAttributes.headPose.yaw', 'faceAttributes.gender',\n",
       "       'faceAttributes.age', 'faceAttributes.facialHair.moustache',\n",
       "       'faceAttributes.facialHair.beard',\n",
       "       'faceAttributes.facialHair.sideburns', 'faceAttributes.glasses',\n",
       "       'faceAttributes.emotion.anger', 'faceAttributes.emotion.contempt',\n",
       "       'faceAttributes.emotion.disgust', 'faceAttributes.emotion.fear',\n",
       "       'faceAttributes.emotion.happiness', 'faceAttributes.emotion.neutral',\n",
       "       'faceAttributes.emotion.sadness', 'faceAttributes.emotion.surprise',\n",
       "       'faceAttributes.blur.blurLevel', 'faceAttributes.blur.value',\n",
       "       'faceAttributes.exposure.exposureLevel',\n",
       "       'faceAttributes.exposure.value', 'faceAttributes.noise.noiseLevel',\n",
       "       'faceAttributes.noise.value', 'faceAttributes.makeup.eyeMakeup',\n",
       "       'faceAttributes.makeup.lipMakeup', 'faceAttributes.accessories',\n",
       "       'faceAttributes.occlusion.foreheadOccluded',\n",
       "       'faceAttributes.occlusion.eyeOccluded',\n",
       "       'faceAttributes.occlusion.mouthOccluded', 'faceAttributes.hair.bald',\n",
       "       'faceAttributes.hair.invisible', 'faceAttributes.hair.hairColor'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查属性/特征值\n",
    "df_face.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faceId</th>\n",
       "      <th>faceAttributes.glasses</th>\n",
       "      <th>faceAttributes.emotion.neutral</th>\n",
       "      <th>faceAttributes.age</th>\n",
       "      <th>faceAttributes.gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c9c0aaf0-534c-413b-a243-feb3c8c483b7</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 faceId faceAttributes.glasses  \\\n",
       "0  c9c0aaf0-534c-413b-a243-feb3c8c483b7              NoGlasses   \n",
       "\n",
       "   faceAttributes.emotion.neutral  faceAttributes.age faceAttributes.gender  \n",
       "0                             0.0                22.0                  male  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可观察其中几组数据\n",
    "df_face[['faceId','faceAttributes.glasses','faceAttributes.emotion.neutral','faceAttributes.age','faceAttributes.gender']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Azure 认知服务-人脸演示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 试一试：人脸验证(难)\n",
    "\n",
    "人脸相似度？有没有试下？\n",
    "\n",
    ">* API人脸文档中最重要的组成部分：\n",
    " >>* Request URL？\n",
    " >>* Http Method？\n",
    " >>* 参数？\n",
    "\n",
    "----\n",
    ">* 不同的人脸对比数据\n",
    "  >>* 人脸验证关键数据？\n",
    "  >>* 根据哪些数据证明是同一个人？\n",
    "    \n",
    "----\n",
    ">* 具体步骤：\n",
    "  >>* 1、Create 请求成功200 返回空字符串\n",
    "  >>* 2、Add face 请求成功200 返回persistedFaceId\n",
    "  >>* 3、Detect 准备 被检测人 人脸的id\n",
    "  >>* 4、Find similars 返回相似置信度\n",
    "  >>* 附加：get查看facelists\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/zhichao02 \n",
      " https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/zhichao02 \n",
      " https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/zhichao02\n"
     ]
    }
   ],
   "source": [
    "# 字符串拼接练习\n",
    "faceListId = \"zhichao02\"\n",
    "# 1\n",
    "url_01 = \"https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/\" + faceListId # string 拼接\n",
    "# 2\n",
    "url_02 = \"https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/%s\" %(faceListId)\n",
    "# 3 \n",
    "url_03 = \"https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/{}\".format(faceListId)\n",
    "print(url_01,'\\n',url_02,'\\n',url_03)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create facelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "# 1、create  list列表\n",
    "# faceListId\n",
    "faceListId =  \"listwy\"# 学生填写设置人脸列表ID\n",
    "create_facelists_url = \"https://api-wy.cognitiveservices.azure.com/face/v1.0/facelists/{}\"# 学生填写 ☆ 注意此条url修改\n",
    "subscription_key = \"d6d151266e1448de9aa63beaa30edd99\"\n",
    "assert subscription_key\n",
    "\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "data = {\n",
    "    \"name\": \"listwy\",\n",
    "    \"userData\": \"WANG\",\n",
    "    \"recognitionModel\": \"recognition_03\" \n",
    "}\n",
    "\n",
    "r_create = requests.put(create_facelists_url.format(faceListId),headers=headers,json=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_create"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_create.content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get facelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-wy.cognitiveservices.azure.com/face/v1.0/facelists/{}\"# 学生填写\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [],\n",
       " 'faceListId': 'listwy',\n",
       " 'name': 'listwy',\n",
       " 'userData': 'WANG'}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add face 请求成功200 返回persistedFaceId\n",
    "> 我们通过上面的步骤建好了一个脸的列表，接下来我们要给这个列表添加脸了！把我们想要对比的脸存进列表吧\n",
    "- [添加人脸进列表api文档](https://docs.microsoft.com/zh-cn/rest/api/cognitiveservices/face/facelist/addfacefromurl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#先加一张脸试试\n",
    "# 2、Add face\n",
    "add_face_url = \"https://api-wy.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedFaces\"\n",
    "subscription_key = \"d6d151266e1448de9aa63beaa30edd99\"\n",
    "assert subscription_key\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "img_url = \"http://img.mp.itc.cn/upload/20170520/a0b0c6c6f52343d2af23689ddca17f66_th.jpg\"\n",
    "\n",
    "params_add_face={\n",
    "    \"faceListId\":\"listwy\",\n",
    "    \"userData\":\"王源\"\n",
    "}\n",
    "\n",
    "r_add_face = requests.post(add_face_url.format(faceListId),headers=headers,params=params_add_face,json={\"url\":img_url})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_add_face.status_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [{'persistedFaceId': '7f5a7bcf-e39a-460d-8f8b-964609fb712f',\n",
       "   'userData': '王源'}],\n",
       " 'faceListId': 'listwy',\n",
       " 'name': 'listwy',\n",
       " 'userData': 'WANG'}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-wy.cognitiveservices.azure.com/face/v1.0/facelists/{}\"\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers)\n",
    "r_get_facelist.json()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 扩展内容，封装成函数方便多次使用 *\n",
    "> 我们要添加多张脸，但是为了减少代码量，我们可以把代码封装成函数，避免每次都要写一大堆代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 封装成函数方便添加图片/函数——可以重复使用相同的功能\n",
    "def AddFace(img_url=str,userData=str):\n",
    "    add_face_url =\"https://api-wy.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedFaces\"\n",
    "    assert subscription_key\n",
    "    headers = {\n",
    "        # Request headers\n",
    "        'Content-Type': 'application/json',\n",
    "        'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "    }\n",
    "    img_url = img_url\n",
    "\n",
    "    params_add_face={\n",
    "        \"userData\":userData\n",
    "    }\n",
    "    r_add_face = requests.post(add_face_url.format(faceListId),headers=headers,params=params_add_face,json={\"url\":img_url})\n",
    "    return r_add_face.status_code#返回出状态码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/Autumnhui.jpg\",\"丘天惠\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/L-Tony-info.jpg\",\"林嘉茵\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/TLINGP.jpg\",\"汤玲萍\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/WenYanZeng.jpg\",\"曾雯燕\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/XIEIC.jpg\",\"谢依希\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/YuecongYang.png\",\"杨悦聪\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/Zoezhouyu.jpg\",\"周雨\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/crayon-heimi.jpg\",\"刘瑜鹏\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/jiayichen.jpg\",\"陈嘉仪\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/kg2000.jpg\",\"徐旖芊\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/liuxinrujiayou.jpg\",\"刘心如\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/liuyu19.png\",\"刘宇\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/ltco.jpg\",\"李婷\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/lucaszy.jpg\",\"黄智毅\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/pingzi0211.jpg\",\"黄慧文\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/shmimy-cn.jpg\",\"张铭睿\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/yichenting.jpg\",\"陈婷\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/coco022.jpg\",\"洪可凡\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/lujizhi.png\",\"卢继志\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/zzlhyy.jpg\",\"张梓乐\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [{'persistedFaceId': '7f5a7bcf-e39a-460d-8f8b-964609fb712f',\n",
       "   'userData': '王源'},\n",
       "  {'persistedFaceId': '8a49775a-3a08-49ae-bcab-531c0de2e5ad',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': '064c96d6-5884-4f4e-9bcb-5bc9dc4a8a09',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': 'c706b414-3c80-438f-a18d-5718050af40c',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': '6ad04350-579c-42b8-9825-59f313178db2',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': '98cb01aa-5dc2-43dd-b1d2-603505d0aa3e',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': 'f41d6dce-fb19-49ad-83b1-ece1ed187635',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': 'fb10322d-4cb0-4eee-b7ee-d0b2f0380a88',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': 'a39a7abe-9a6d-41dc-add0-bc224768a188',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': 'ec3bf71c-6100-433a-a139-ed8a6e8d51ae',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': 'a837eae0-cc0c-4ca1-ba75-e0a30abf1326',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': 'd61b3ed4-b160-4f73-8289-e1bec05534b7',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': '5b7d40d5-7035-4b88-ab85-b24f4367e197',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '3f5bba60-aefa-425a-99b0-65e039e2953c',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '061b1afe-72d8-40da-af0f-6ba87157b99f',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '0109fce1-9506-4ce1-b35c-7076afaea087',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': '4aec25b3-c400-441c-96e9-4931a9de53a0',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '0ebb0e46-d4cc-42f7-9e9a-05e357835431',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '5769cfd3-d5d8-4cad-8681-510c9c5816ff',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': 'a6e22a47-3bf0-432b-ba3d-031253339d4f',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': '908b2e6e-ee89-4e93-9bce-ff334ae324a8',\n",
       "   'userData': '张梓乐'},\n",
       "  {'persistedFaceId': 'f7d3b273-55f0-4d05-af9d-ff6f920dcb21',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': 'c72f1ed4-a7ec-41a6-a4c4-08c3edcb30f6',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '63bc0338-3200-4da8-bbf6-2e1b03765f3e',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': 'd8839a48-dfc0-48c9-bda0-88479b7212b7',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': '880dcb68-8d53-4beb-80f6-af60fe099360',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': '8d16c3f2-b856-4d3c-aafa-7193784e7801',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '8f163eb4-5fdb-4f0e-9d63-4876ee616b4a',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '069b3cb9-725e-42b1-82f0-5699e438ff9a',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': 'ee6e173f-e0e6-4444-baaa-e812c7ce6c3a',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '997cb8cf-3de3-4e21-9268-778e24472ca7',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '210cb1b6-587b-4b75-aa4d-1624bee45334',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': 'dac1b627-2e28-4765-b6b2-e9a0ece45870',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '787b7e54-21a2-479e-ab97-2801524f0a3d',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '49292bfb-5c75-4ce0-83aa-7d906ee7e5e0',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '35924ddf-07ec-427d-8d0f-348e38deea28',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': 'a4489af7-31fb-49db-8a3e-d878955be9da',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '13e04f1b-8ff6-4120-a63a-f4e75a7023d7',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '1e8bd26f-371a-4d77-a0ca-61b0ed77c238',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': '56dcc6be-d236-41f0-87eb-452e4acfb91c',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': '13a07365-4862-4af9-851c-faf35d45cd17',\n",
       "   'userData': '张梓乐'},\n",
       "  {'persistedFaceId': '77dfc9ce-d6c1-4663-a780-1cea9f14f69e',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': '9e916d79-9ce2-4da1-8a3a-0eb4442881bf',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '42c34230-4b3a-4416-9801-89f8047cc149',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': 'ce7870ea-90ab-4bf0-828a-2ef796861bf5',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': '008b267d-f489-4178-9e3b-0291f9ddc4e4',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': 'af7e9fb6-cddd-46f5-a27f-7b26ee9d11fe',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': 'a77ff82c-bc02-4428-a2ba-132e11f72edf',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': 'eb721d2e-a0e6-402b-988c-213e44395446',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': 'ca099425-89f0-4127-a2be-172d8c791ad3',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '51bac144-0eb1-45be-a0f7-534389dcc3d5',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': 'd936b2e5-d984-45e6-82e0-8c74fea912ad',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': 'd62c598d-b2e5-466a-96dd-9f240c7bfb27',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '6b11e257-c017-4638-abcd-c0604328b40a',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '9981eba3-6626-42b9-b9c4-b00681f42a05',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '51ae8d42-56e5-49e2-ad0a-9aee793f29e9',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': 'b9e519aa-28a6-440f-a0a9-f376875012d8',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '4d857e15-e1af-49bf-bef4-5e62c0cdcc89',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '2596485b-c8ef-4431-a07e-d735c111f1d8',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': '82d82419-25de-403c-990d-d5dcbf757230',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': 'd2ded184-5dff-498e-992e-c287ce7befe9',\n",
       "   'userData': '张梓乐'},\n",
       "  {'persistedFaceId': '3267b059-516c-432f-b221-0aac8f711873',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': '1f810160-6e54-48fa-a780-46750d03fb02',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '78df1e2a-6c64-4508-8629-41f6fbbcf9c6',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': 'daf9508a-b4f9-4890-b3c3-cd720dc14beb',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': 'a67b9223-90f3-4bdc-8e1d-04409fd5441e',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': 'cd8eaf54-dd67-4a52-9d12-935489647ce3',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '4b21af0c-53ce-46b2-9d38-fb95d6223a36',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': 'b66bef86-acc7-4bd5-96f2-fb547aa1415d',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '63e7866c-c5f8-45e8-9872-59b4506062f2',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '14c72f5e-d45b-4fb0-aeb4-54eb62f9829e',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '6310ccdb-85df-492d-957b-c64987dc2e1a',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': '083a459e-9195-414e-a923-80f56daa0559',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '0304ae0b-088b-4fac-976b-53287d35cdd8',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '3c13e791-4905-401b-915e-e3861f165c4d',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': 'cc9f5c03-3650-4b05-bccc-41a9bbf61da9',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': '9b65f6e7-d62d-4712-af37-79f2989187fa',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '4cc2cd1d-cef0-4afd-aa90-0a0bff26342c',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '2faf83f8-bdb7-4b35-8fbc-cdda64280ca0',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': '4499afe3-091a-4655-a3c1-c0b58f6d9277',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': '4fb970e0-b4bd-458d-8758-5f89c3aed4b9',\n",
       "   'userData': '张梓乐'},\n",
       "  {'persistedFaceId': '02bb9fdb-d68e-45df-827d-e3952485bf62',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': '30b5b43f-b688-40fa-b80f-0a5327442d61',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '80ad460a-0ae9-405e-9f69-fdd3548606b9',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': '7df023c2-233f-4890-809c-24e1a5df4057',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': 'a0ff6a48-8c33-44c2-a53d-a74ac925b429',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': '2365171e-c542-44de-8656-0183bf759752',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '7430d422-0db6-4a9a-86ea-8440448f82a0',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '579d4848-69d5-48ce-90b4-20f0e7f29adc',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '235a1163-a539-49e2-b988-65b43a0018d3',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '8deca61b-f61e-434f-babf-bb1f3e363580',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '717106be-fe43-41b4-9d95-1eff953bf260',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': 'ba9625be-a184-4d29-b494-ec74f7a4387f',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': 'bbedef87-5864-4960-abe3-8063df929020',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': 'ed4b4ea9-39e4-46a5-b3da-915f9c2d2065',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '731d07dc-ec0b-4712-b17e-c516020f5916',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': 'd29f30c8-2cb2-4506-a0be-34d0da674043',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '182045d6-4546-43ab-b55d-24d8f65087cd',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '59ad3c7d-74a8-4adf-a73d-abf68e41f6a2',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': 'c7587661-d192-4f89-b2dd-8b9a045d1e79',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': 'b2f67c98-38a9-4793-bd17-aa99815b1046',\n",
       "   'userData': '张梓乐'}],\n",
       " 'faceListId': 'listwy',\n",
       " 'name': 'listwy',\n",
       " 'userData': 'WANG'}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-wy.cognitiveservices.azure.com/face/v1.0/facelists/{}\"\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers)\n",
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1f810160-6e54-48fa-a780-46750d03fb02'"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 键/值\n",
    "for i in faceId:\n",
    "#     print(i)\n",
    "    if i[\"userData\"] == \"林嘉茵\":\n",
    "        faceId_02 = i['persistedFaceId']\n",
    "faceId_02\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'persistedFaceId': '7f5a7bcf-e39a-460d-8f8b-964609fb712f', 'userData': '王源'},\n",
       " {'persistedFaceId': '8a49775a-3a08-49ae-bcab-531c0de2e5ad',\n",
       "  'userData': '丘天惠'},\n",
       " {'persistedFaceId': '064c96d6-5884-4f4e-9bcb-5bc9dc4a8a09',\n",
       "  'userData': '林嘉茵'},\n",
       " {'persistedFaceId': 'c706b414-3c80-438f-a18d-5718050af40c',\n",
       "  'userData': '汤玲萍'},\n",
       " {'persistedFaceId': '6ad04350-579c-42b8-9825-59f313178db2',\n",
       "  'userData': '曾雯燕'},\n",
       " {'persistedFaceId': '98cb01aa-5dc2-43dd-b1d2-603505d0aa3e',\n",
       "  'userData': '谢依希'},\n",
       " {'persistedFaceId': 'f41d6dce-fb19-49ad-83b1-ece1ed187635',\n",
       "  'userData': '杨悦聪'},\n",
       " {'persistedFaceId': 'fb10322d-4cb0-4eee-b7ee-d0b2f0380a88', 'userData': '周雨'},\n",
       " {'persistedFaceId': 'a39a7abe-9a6d-41dc-add0-bc224768a188',\n",
       "  'userData': '刘瑜鹏'},\n",
       " {'persistedFaceId': 'ec3bf71c-6100-433a-a139-ed8a6e8d51ae',\n",
       "  'userData': '陈嘉仪'},\n",
       " {'persistedFaceId': 'a837eae0-cc0c-4ca1-ba75-e0a30abf1326',\n",
       "  'userData': '徐旖芊'},\n",
       " {'persistedFaceId': 'd61b3ed4-b160-4f73-8289-e1bec05534b7',\n",
       "  'userData': '刘心如'},\n",
       " {'persistedFaceId': '5b7d40d5-7035-4b88-ab85-b24f4367e197', 'userData': '刘宇'},\n",
       " {'persistedFaceId': '3f5bba60-aefa-425a-99b0-65e039e2953c', 'userData': '李婷'},\n",
       " {'persistedFaceId': '061b1afe-72d8-40da-af0f-6ba87157b99f',\n",
       "  'userData': '黄智毅'},\n",
       " {'persistedFaceId': '0109fce1-9506-4ce1-b35c-7076afaea087',\n",
       "  'userData': '黄慧文'},\n",
       " {'persistedFaceId': '4aec25b3-c400-441c-96e9-4931a9de53a0',\n",
       "  'userData': '张铭睿'},\n",
       " {'persistedFaceId': '0ebb0e46-d4cc-42f7-9e9a-05e357835431', 'userData': '陈婷'},\n",
       " {'persistedFaceId': '5769cfd3-d5d8-4cad-8681-510c9c5816ff',\n",
       "  'userData': '洪可凡'},\n",
       " {'persistedFaceId': 'a6e22a47-3bf0-432b-ba3d-031253339d4f',\n",
       "  'userData': '卢继志'},\n",
       " {'persistedFaceId': '908b2e6e-ee89-4e93-9bce-ff334ae324a8',\n",
       "  'userData': '张梓乐'},\n",
       " {'persistedFaceId': 'f7d3b273-55f0-4d05-af9d-ff6f920dcb21',\n",
       "  'userData': '丘天惠'},\n",
       " {'persistedFaceId': 'c72f1ed4-a7ec-41a6-a4c4-08c3edcb30f6',\n",
       "  'userData': '林嘉茵'},\n",
       " {'persistedFaceId': '63bc0338-3200-4da8-bbf6-2e1b03765f3e',\n",
       "  'userData': '汤玲萍'},\n",
       " {'persistedFaceId': 'd8839a48-dfc0-48c9-bda0-88479b7212b7',\n",
       "  'userData': '曾雯燕'},\n",
       " {'persistedFaceId': '880dcb68-8d53-4beb-80f6-af60fe099360',\n",
       "  'userData': '谢依希'},\n",
       " {'persistedFaceId': '8d16c3f2-b856-4d3c-aafa-7193784e7801',\n",
       "  'userData': '杨悦聪'},\n",
       " {'persistedFaceId': '8f163eb4-5fdb-4f0e-9d63-4876ee616b4a', 'userData': '周雨'},\n",
       " {'persistedFaceId': '069b3cb9-725e-42b1-82f0-5699e438ff9a',\n",
       "  'userData': '刘瑜鹏'},\n",
       " {'persistedFaceId': 'ee6e173f-e0e6-4444-baaa-e812c7ce6c3a',\n",
       "  'userData': '陈嘉仪'},\n",
       " {'persistedFaceId': '997cb8cf-3de3-4e21-9268-778e24472ca7',\n",
       "  'userData': '徐旖芊'},\n",
       " {'persistedFaceId': '210cb1b6-587b-4b75-aa4d-1624bee45334',\n",
       "  'userData': '刘心如'},\n",
       " {'persistedFaceId': 'dac1b627-2e28-4765-b6b2-e9a0ece45870', 'userData': '刘宇'},\n",
       " {'persistedFaceId': '787b7e54-21a2-479e-ab97-2801524f0a3d', 'userData': '李婷'},\n",
       " {'persistedFaceId': '49292bfb-5c75-4ce0-83aa-7d906ee7e5e0',\n",
       "  'userData': '黄智毅'},\n",
       " {'persistedFaceId': '35924ddf-07ec-427d-8d0f-348e38deea28',\n",
       "  'userData': '黄慧文'},\n",
       " {'persistedFaceId': 'a4489af7-31fb-49db-8a3e-d878955be9da',\n",
       "  'userData': '张铭睿'},\n",
       " {'persistedFaceId': '13e04f1b-8ff6-4120-a63a-f4e75a7023d7', 'userData': '陈婷'},\n",
       " {'persistedFaceId': '1e8bd26f-371a-4d77-a0ca-61b0ed77c238',\n",
       "  'userData': '洪可凡'},\n",
       " {'persistedFaceId': '56dcc6be-d236-41f0-87eb-452e4acfb91c',\n",
       "  'userData': '卢继志'},\n",
       " {'persistedFaceId': '13a07365-4862-4af9-851c-faf35d45cd17',\n",
       "  'userData': '张梓乐'},\n",
       " {'persistedFaceId': '77dfc9ce-d6c1-4663-a780-1cea9f14f69e',\n",
       "  'userData': '丘天惠'},\n",
       " {'persistedFaceId': '9e916d79-9ce2-4da1-8a3a-0eb4442881bf',\n",
       "  'userData': '林嘉茵'},\n",
       " {'persistedFaceId': '42c34230-4b3a-4416-9801-89f8047cc149',\n",
       "  'userData': '汤玲萍'},\n",
       " {'persistedFaceId': 'ce7870ea-90ab-4bf0-828a-2ef796861bf5',\n",
       "  'userData': '曾雯燕'},\n",
       " {'persistedFaceId': '008b267d-f489-4178-9e3b-0291f9ddc4e4',\n",
       "  'userData': '谢依希'},\n",
       " {'persistedFaceId': 'af7e9fb6-cddd-46f5-a27f-7b26ee9d11fe',\n",
       "  'userData': '杨悦聪'},\n",
       " {'persistedFaceId': 'a77ff82c-bc02-4428-a2ba-132e11f72edf', 'userData': '周雨'},\n",
       " {'persistedFaceId': 'eb721d2e-a0e6-402b-988c-213e44395446',\n",
       "  'userData': '刘瑜鹏'},\n",
       " {'persistedFaceId': 'ca099425-89f0-4127-a2be-172d8c791ad3',\n",
       "  'userData': '陈嘉仪'},\n",
       " {'persistedFaceId': '51bac144-0eb1-45be-a0f7-534389dcc3d5',\n",
       "  'userData': '徐旖芊'},\n",
       " {'persistedFaceId': 'd936b2e5-d984-45e6-82e0-8c74fea912ad',\n",
       "  'userData': '刘心如'},\n",
       " {'persistedFaceId': 'd62c598d-b2e5-466a-96dd-9f240c7bfb27', 'userData': '刘宇'},\n",
       " {'persistedFaceId': '6b11e257-c017-4638-abcd-c0604328b40a', 'userData': '李婷'},\n",
       " {'persistedFaceId': '9981eba3-6626-42b9-b9c4-b00681f42a05',\n",
       "  'userData': '黄智毅'},\n",
       " {'persistedFaceId': '51ae8d42-56e5-49e2-ad0a-9aee793f29e9',\n",
       "  'userData': '黄慧文'},\n",
       " {'persistedFaceId': 'b9e519aa-28a6-440f-a0a9-f376875012d8',\n",
       "  'userData': '张铭睿'},\n",
       " {'persistedFaceId': '4d857e15-e1af-49bf-bef4-5e62c0cdcc89', 'userData': '陈婷'},\n",
       " {'persistedFaceId': '2596485b-c8ef-4431-a07e-d735c111f1d8',\n",
       "  'userData': '洪可凡'},\n",
       " {'persistedFaceId': '82d82419-25de-403c-990d-d5dcbf757230',\n",
       "  'userData': '卢继志'},\n",
       " {'persistedFaceId': 'd2ded184-5dff-498e-992e-c287ce7befe9',\n",
       "  'userData': '张梓乐'},\n",
       " {'persistedFaceId': '3267b059-516c-432f-b221-0aac8f711873',\n",
       "  'userData': '丘天惠'},\n",
       " {'persistedFaceId': '1f810160-6e54-48fa-a780-46750d03fb02',\n",
       "  'userData': '林嘉茵'},\n",
       " {'persistedFaceId': '78df1e2a-6c64-4508-8629-41f6fbbcf9c6',\n",
       "  'userData': '汤玲萍'},\n",
       " {'persistedFaceId': 'daf9508a-b4f9-4890-b3c3-cd720dc14beb',\n",
       "  'userData': '曾雯燕'},\n",
       " {'persistedFaceId': 'a67b9223-90f3-4bdc-8e1d-04409fd5441e',\n",
       "  'userData': '谢依希'},\n",
       " {'persistedFaceId': 'cd8eaf54-dd67-4a52-9d12-935489647ce3',\n",
       "  'userData': '杨悦聪'},\n",
       " {'persistedFaceId': '4b21af0c-53ce-46b2-9d38-fb95d6223a36', 'userData': '周雨'},\n",
       " {'persistedFaceId': 'b66bef86-acc7-4bd5-96f2-fb547aa1415d',\n",
       "  'userData': '刘瑜鹏'},\n",
       " {'persistedFaceId': '63e7866c-c5f8-45e8-9872-59b4506062f2',\n",
       "  'userData': '陈嘉仪'},\n",
       " {'persistedFaceId': '14c72f5e-d45b-4fb0-aeb4-54eb62f9829e',\n",
       "  'userData': '徐旖芊'},\n",
       " {'persistedFaceId': '6310ccdb-85df-492d-957b-c64987dc2e1a',\n",
       "  'userData': '刘心如'},\n",
       " {'persistedFaceId': '083a459e-9195-414e-a923-80f56daa0559', 'userData': '刘宇'},\n",
       " {'persistedFaceId': '0304ae0b-088b-4fac-976b-53287d35cdd8', 'userData': '李婷'},\n",
       " {'persistedFaceId': '3c13e791-4905-401b-915e-e3861f165c4d',\n",
       "  'userData': '黄智毅'},\n",
       " {'persistedFaceId': 'cc9f5c03-3650-4b05-bccc-41a9bbf61da9',\n",
       "  'userData': '黄慧文'},\n",
       " {'persistedFaceId': '9b65f6e7-d62d-4712-af37-79f2989187fa',\n",
       "  'userData': '张铭睿'},\n",
       " {'persistedFaceId': '4cc2cd1d-cef0-4afd-aa90-0a0bff26342c', 'userData': '陈婷'},\n",
       " {'persistedFaceId': '2faf83f8-bdb7-4b35-8fbc-cdda64280ca0',\n",
       "  'userData': '洪可凡'},\n",
       " {'persistedFaceId': '4499afe3-091a-4655-a3c1-c0b58f6d9277',\n",
       "  'userData': '卢继志'},\n",
       " {'persistedFaceId': '4fb970e0-b4bd-458d-8758-5f89c3aed4b9',\n",
       "  'userData': '张梓乐'},\n",
       " {'persistedFaceId': '02bb9fdb-d68e-45df-827d-e3952485bf62',\n",
       "  'userData': '丘天惠'},\n",
       " {'persistedFaceId': '30b5b43f-b688-40fa-b80f-0a5327442d61',\n",
       "  'userData': '林嘉茵'},\n",
       " {'persistedFaceId': '80ad460a-0ae9-405e-9f69-fdd3548606b9',\n",
       "  'userData': '汤玲萍'},\n",
       " {'persistedFaceId': '7df023c2-233f-4890-809c-24e1a5df4057',\n",
       "  'userData': '曾雯燕'},\n",
       " {'persistedFaceId': 'a0ff6a48-8c33-44c2-a53d-a74ac925b429',\n",
       "  'userData': '谢依希'},\n",
       " {'persistedFaceId': '2365171e-c542-44de-8656-0183bf759752',\n",
       "  'userData': '杨悦聪'},\n",
       " {'persistedFaceId': '7430d422-0db6-4a9a-86ea-8440448f82a0', 'userData': '周雨'},\n",
       " {'persistedFaceId': '579d4848-69d5-48ce-90b4-20f0e7f29adc',\n",
       "  'userData': '刘瑜鹏'},\n",
       " {'persistedFaceId': '235a1163-a539-49e2-b988-65b43a0018d3',\n",
       "  'userData': '陈嘉仪'},\n",
       " {'persistedFaceId': '8deca61b-f61e-434f-babf-bb1f3e363580',\n",
       "  'userData': '徐旖芊'},\n",
       " {'persistedFaceId': '717106be-fe43-41b4-9d95-1eff953bf260',\n",
       "  'userData': '刘心如'},\n",
       " {'persistedFaceId': 'ba9625be-a184-4d29-b494-ec74f7a4387f', 'userData': '刘宇'},\n",
       " {'persistedFaceId': 'bbedef87-5864-4960-abe3-8063df929020', 'userData': '李婷'},\n",
       " {'persistedFaceId': 'ed4b4ea9-39e4-46a5-b3da-915f9c2d2065',\n",
       "  'userData': '黄智毅'},\n",
       " {'persistedFaceId': '731d07dc-ec0b-4712-b17e-c516020f5916',\n",
       "  'userData': '黄慧文'},\n",
       " {'persistedFaceId': 'd29f30c8-2cb2-4506-a0be-34d0da674043',\n",
       "  'userData': '张铭睿'},\n",
       " {'persistedFaceId': '182045d6-4546-43ab-b55d-24d8f65087cd', 'userData': '陈婷'},\n",
       " {'persistedFaceId': '59ad3c7d-74a8-4adf-a73d-abf68e41f6a2',\n",
       "  'userData': '洪可凡'},\n",
       " {'persistedFaceId': 'c7587661-d192-4f89-b2dd-8b9a045d1e79',\n",
       "  'userData': '卢继志'},\n",
       " {'persistedFaceId': 'b2f67c98-38a9-4793-bd17-aa99815b1046',\n",
       "  'userData': '张梓乐'}]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceId =  r_get_facelist.json()['persistedFaces']\n",
    "faceId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "061b1afe-72d8-40da-af0f-6ba87157b99f\n",
      "49292bfb-5c75-4ce0-83aa-7d906ee7e5e0\n",
      "9981eba3-6626-42b9-b9c4-b00681f42a05\n",
      "3c13e791-4905-401b-915e-e3861f165c4d\n",
      "ed4b4ea9-39e4-46a5-b3da-915f9c2d2065\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'ed4b4ea9-39e4-46a5-b3da-915f9c2d2065'"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for item in faceId:\n",
    "#     print(item)\n",
    "    if item['userData'] == '黄智毅':\n",
    "        print(item['persistedFaceId'])\n",
    "        delate_face = item['persistedFaceId']\n",
    "delate_face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### delate face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detect face 删除列表内人脸id\n",
    "faceListId = \"listwy\"\n",
    "delete_face_url = \"https://api-wy.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedFaces/{}\"\n",
    "\n",
    "assert subscription_key\n",
    "# 例如：删除黄志毅： {'persistedFaceId': 'f18450d3-60d2-45f3-a69e-783574dc3ce8','userData': '黄智毅'},\n",
    "\n",
    "\n",
    "persistedFaceId = r_add_face.json()['persistedFaceId']\n",
    "# 直接取上面获得的ID{'persistedFaceId': 'f18450d3-60d2-45f3-a69e-783574dc3ce8'} \n",
    "\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "\n",
    "# 注意requests请求为delete\n",
    "r_delete_face = requests.delete(delete_face_url.format(faceListId,persistedFaceId,delate_face),headers=headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_delete_face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [{'persistedFaceId': '8a49775a-3a08-49ae-bcab-531c0de2e5ad',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': '064c96d6-5884-4f4e-9bcb-5bc9dc4a8a09',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': 'c706b414-3c80-438f-a18d-5718050af40c',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': '6ad04350-579c-42b8-9825-59f313178db2',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': '98cb01aa-5dc2-43dd-b1d2-603505d0aa3e',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': 'f41d6dce-fb19-49ad-83b1-ece1ed187635',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': 'fb10322d-4cb0-4eee-b7ee-d0b2f0380a88',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': 'a39a7abe-9a6d-41dc-add0-bc224768a188',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': 'ec3bf71c-6100-433a-a139-ed8a6e8d51ae',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': 'a837eae0-cc0c-4ca1-ba75-e0a30abf1326',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': 'd61b3ed4-b160-4f73-8289-e1bec05534b7',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': '5b7d40d5-7035-4b88-ab85-b24f4367e197',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '3f5bba60-aefa-425a-99b0-65e039e2953c',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '061b1afe-72d8-40da-af0f-6ba87157b99f',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '0109fce1-9506-4ce1-b35c-7076afaea087',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': '4aec25b3-c400-441c-96e9-4931a9de53a0',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '0ebb0e46-d4cc-42f7-9e9a-05e357835431',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '5769cfd3-d5d8-4cad-8681-510c9c5816ff',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': 'a6e22a47-3bf0-432b-ba3d-031253339d4f',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': '908b2e6e-ee89-4e93-9bce-ff334ae324a8',\n",
       "   'userData': '张梓乐'},\n",
       "  {'persistedFaceId': 'f7d3b273-55f0-4d05-af9d-ff6f920dcb21',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': 'c72f1ed4-a7ec-41a6-a4c4-08c3edcb30f6',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '63bc0338-3200-4da8-bbf6-2e1b03765f3e',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': 'd8839a48-dfc0-48c9-bda0-88479b7212b7',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': '880dcb68-8d53-4beb-80f6-af60fe099360',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': '8d16c3f2-b856-4d3c-aafa-7193784e7801',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '8f163eb4-5fdb-4f0e-9d63-4876ee616b4a',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '069b3cb9-725e-42b1-82f0-5699e438ff9a',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': 'ee6e173f-e0e6-4444-baaa-e812c7ce6c3a',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '997cb8cf-3de3-4e21-9268-778e24472ca7',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '210cb1b6-587b-4b75-aa4d-1624bee45334',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': 'dac1b627-2e28-4765-b6b2-e9a0ece45870',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '787b7e54-21a2-479e-ab97-2801524f0a3d',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '49292bfb-5c75-4ce0-83aa-7d906ee7e5e0',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '35924ddf-07ec-427d-8d0f-348e38deea28',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': 'a4489af7-31fb-49db-8a3e-d878955be9da',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '13e04f1b-8ff6-4120-a63a-f4e75a7023d7',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '1e8bd26f-371a-4d77-a0ca-61b0ed77c238',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': '56dcc6be-d236-41f0-87eb-452e4acfb91c',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': '13a07365-4862-4af9-851c-faf35d45cd17',\n",
       "   'userData': '张梓乐'},\n",
       "  {'persistedFaceId': '77dfc9ce-d6c1-4663-a780-1cea9f14f69e',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': '9e916d79-9ce2-4da1-8a3a-0eb4442881bf',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '42c34230-4b3a-4416-9801-89f8047cc149',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': 'ce7870ea-90ab-4bf0-828a-2ef796861bf5',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': '008b267d-f489-4178-9e3b-0291f9ddc4e4',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': 'af7e9fb6-cddd-46f5-a27f-7b26ee9d11fe',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': 'a77ff82c-bc02-4428-a2ba-132e11f72edf',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': 'eb721d2e-a0e6-402b-988c-213e44395446',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': 'ca099425-89f0-4127-a2be-172d8c791ad3',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '51bac144-0eb1-45be-a0f7-534389dcc3d5',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': 'd936b2e5-d984-45e6-82e0-8c74fea912ad',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': 'd62c598d-b2e5-466a-96dd-9f240c7bfb27',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '6b11e257-c017-4638-abcd-c0604328b40a',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '9981eba3-6626-42b9-b9c4-b00681f42a05',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '51ae8d42-56e5-49e2-ad0a-9aee793f29e9',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': 'b9e519aa-28a6-440f-a0a9-f376875012d8',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '4d857e15-e1af-49bf-bef4-5e62c0cdcc89',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '2596485b-c8ef-4431-a07e-d735c111f1d8',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': '82d82419-25de-403c-990d-d5dcbf757230',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': 'd2ded184-5dff-498e-992e-c287ce7befe9',\n",
       "   'userData': '张梓乐'},\n",
       "  {'persistedFaceId': '3267b059-516c-432f-b221-0aac8f711873',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': '1f810160-6e54-48fa-a780-46750d03fb02',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '78df1e2a-6c64-4508-8629-41f6fbbcf9c6',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': 'daf9508a-b4f9-4890-b3c3-cd720dc14beb',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': 'a67b9223-90f3-4bdc-8e1d-04409fd5441e',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': 'cd8eaf54-dd67-4a52-9d12-935489647ce3',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '4b21af0c-53ce-46b2-9d38-fb95d6223a36',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': 'b66bef86-acc7-4bd5-96f2-fb547aa1415d',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '63e7866c-c5f8-45e8-9872-59b4506062f2',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '14c72f5e-d45b-4fb0-aeb4-54eb62f9829e',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '6310ccdb-85df-492d-957b-c64987dc2e1a',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': '083a459e-9195-414e-a923-80f56daa0559',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '0304ae0b-088b-4fac-976b-53287d35cdd8',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '3c13e791-4905-401b-915e-e3861f165c4d',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': 'cc9f5c03-3650-4b05-bccc-41a9bbf61da9',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': '9b65f6e7-d62d-4712-af37-79f2989187fa',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '4cc2cd1d-cef0-4afd-aa90-0a0bff26342c',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '2faf83f8-bdb7-4b35-8fbc-cdda64280ca0',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': '4499afe3-091a-4655-a3c1-c0b58f6d9277',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': '4fb970e0-b4bd-458d-8758-5f89c3aed4b9',\n",
       "   'userData': '张梓乐'},\n",
       "  {'persistedFaceId': '02bb9fdb-d68e-45df-827d-e3952485bf62',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': '30b5b43f-b688-40fa-b80f-0a5327442d61',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '80ad460a-0ae9-405e-9f69-fdd3548606b9',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': '7df023c2-233f-4890-809c-24e1a5df4057',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': 'a0ff6a48-8c33-44c2-a53d-a74ac925b429',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': '2365171e-c542-44de-8656-0183bf759752',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '7430d422-0db6-4a9a-86ea-8440448f82a0',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '579d4848-69d5-48ce-90b4-20f0e7f29adc',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '235a1163-a539-49e2-b988-65b43a0018d3',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '8deca61b-f61e-434f-babf-bb1f3e363580',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '717106be-fe43-41b4-9d95-1eff953bf260',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': 'ba9625be-a184-4d29-b494-ec74f7a4387f',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': 'bbedef87-5864-4960-abe3-8063df929020',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': 'ed4b4ea9-39e4-46a5-b3da-915f9c2d2065',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '731d07dc-ec0b-4712-b17e-c516020f5916',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': 'd29f30c8-2cb2-4506-a0be-34d0da674043',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '182045d6-4546-43ab-b55d-24d8f65087cd',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '59ad3c7d-74a8-4adf-a73d-abf68e41f6a2',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': 'c7587661-d192-4f89-b2dd-8b9a045d1e79',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': 'b2f67c98-38a9-4793-bd17-aa99815b1046',\n",
       "   'userData': '张梓乐'}],\n",
       " 'faceListId': 'listwy',\n",
       " 'name': 'listwy',\n",
       " 'userData': 'WANG'}"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-wy.cognitiveservices.azure.com/face/v1.0/facelists/{}\"\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers)\n",
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find similars 返回相似置信度\n",
    "- [监测人脸相似度api文档](https://docs.microsoft.com/zh-cn/rest/api/cognitiveservices/face/face/findsimilar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"faceId\": \"e97b7f1e-a5f0-4aac-8ed3-5e038417b734\", \"faceRectangle\": {\"top\": 297, \"left\": 142, \"width\": 557, \"height\": 557}, \"faceAttributes\": {\"smile\": 0.0, \"headPose\": {\"pitch\": -2.0, \"roll\": 13.4, \"yaw\": -16.6}, \"gender\": \"male\", \"age\": 19.0, \"facialHair\": {\"moustache\": 0.1, \"beard\": 0.1, \"sideburns\": 0.1}, \"glasses\": \"NoGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.0, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 0.0, \"neutral\": 1.0, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.16}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.61}, \"noise\": {\"noiseLevel\": \"medium\", \"value\": 0.48}, \"makeup\": {\"eyeMakeup\": true, \"lipMakeup\": true}, \"accessories\": [], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.1, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 1.0}, {\"color\": \"other\", \"confidence\": 0.58}, {\"color\": \"brown\", \"confidence\": 0.54}, {\"color\": \"gray\", \"confidence\": 0.41}, {\"color\": \"blond\", \"confidence\": 0.04}, {\"color\": \"red\", \"confidence\": 0.02}, {\"color\": \"white\", \"confidence\": 0.0}]}}}]'"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detect 检测人脸的id\n",
    "# replace <My Endpoint String> with the string from your endpoint URL\n",
    "import json\n",
    "face_api_url = 'https://api-wy.cognitiveservices.azure.com/face/v1.0/detect'\n",
    "\n",
    "# 请求正文\n",
    "image_url = \"http://img.mp.itc.cn/upload/20170520/a0b0c6c6f52343d2af23689ddca17f66_th.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "\n",
    "# 请求参数\n",
    "params = {\n",
    "    'returnFaceId': 'true',\n",
    "    'returnFaceLandmarks': 'false',\n",
    "    # 选择model\n",
    "    'recognitionModel':'recognition_03',#此参数需与facelist参数一致\n",
    "    'detectionModel':'detection_01',\n",
    "    # 可选参数,请仔细阅读API文档\n",
    "    'returnFaceAttributes': 'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise',\n",
    "}\n",
    "\n",
    "response = requests.post(face_api_url.format(faceListId), params=params,\n",
    "                         headers=headers, json={\"url\": image_url})\n",
    "# json.dumps 将json--->字符串\n",
    "json.dumps(response.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "findsimilars_url = \"https://api-wy.cognitiveservices.azure.com/face/v1.0/findsimilars\"\n",
    "\n",
    "# 请求正文 faceId需要先检测一张照片获取\n",
    "data_findsimilars = {\n",
    "    \"faceId\":response.json()[0][\"faceId\"],#取上方的faceID\n",
    "    \"faceListId\": \"listwy\",\n",
    "#     \"faceIds\":faceId_02,\n",
    "    \"maxNumOfCandidatesReturned\": 10,\n",
    "    \"mode\": \"matchPerson\"#matchPerson #一种为验证模式，一种为相似值模式\n",
    "    }\n",
    "\n",
    "r_findsimilars = requests.post(findsimilars_url.format(faceListId,faceId),headers=headers,json=data_findsimilars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_findsimilars.status_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_findsimilars.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>persistedFaceId</th>\n",
       "      <th>userData</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5081698a-efe4-4be9-822c-cb58f2cc4803</td>\n",
       "      <td>丘某峰</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8cfa1b6d-b087-4cd2-be8c-55582168b497</td>\n",
       "      <td>丘天惠</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8a559e64-a44b-4a4b-84dc-e14da959da3a</td>\n",
       "      <td>林嘉茵</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7f5db725-735d-4b4f-a91b-42ac215b5aa9</td>\n",
       "      <td>汤玲萍</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e386e027-8299-4703-a748-8ab7d060348c</td>\n",
       "      <td>曾雯燕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6d3cb16e-239d-495a-a3cc-b6f346b07034</td>\n",
       "      <td>谢依希</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>81ec76dd-163f-4ee3-aa16-11a0ce73295e</td>\n",
       "      <td>杨悦聪</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6c940c74-645a-45c8-884d-6ab34ba352c2</td>\n",
       "      <td>周雨</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>d8c64294-0629-4e81-87d5-3976ad54ac0e</td>\n",
       "      <td>刘瑜鹏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>c0ee4410-bca0-4a3f-acb5-74c8eb72f0a1</td>\n",
       "      <td>陈嘉仪</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4278a690-5f78-40d7-abe5-88eab5d5494f</td>\n",
       "      <td>徐旖芊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>e61d920f-f131-464a-9a43-a82babf20358</td>\n",
       "      <td>刘心如</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>a968313c-ab4e-42d0-9265-749dcf9b8529</td>\n",
       "      <td>刘宇</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8b1c2538-05cc-4636-8460-cfa8059d5c72</td>\n",
       "      <td>李婷</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>d0c25191-8224-410e-9eed-df3457e0a77f</td>\n",
       "      <td>黄智毅</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>454e8382-9ee5-4264-b97f-02ecbe5bd987</td>\n",
       "      <td>黄慧文</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5bfad450-0274-40b5-ba9f-0e4a0af9763b</td>\n",
       "      <td>张铭睿</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>a9c9451c-8627-4eed-b587-9410d5a61ff9</td>\n",
       "      <td>陈婷</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6a663a8e-725c-4c7d-bd72-5520c4a2e93e</td>\n",
       "      <td>洪可凡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>050bf519-7cdf-4b6a-8aac-52f475d282f8</td>\n",
       "      <td>卢继志</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0523267b-6750-4d1e-987d-674e0ecb0821</td>\n",
       "      <td>张梓乐</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         persistedFaceId userData\n",
       "0   5081698a-efe4-4be9-822c-cb58f2cc4803      丘某峰\n",
       "1   8cfa1b6d-b087-4cd2-be8c-55582168b497      丘天惠\n",
       "2   8a559e64-a44b-4a4b-84dc-e14da959da3a      林嘉茵\n",
       "3   7f5db725-735d-4b4f-a91b-42ac215b5aa9      汤玲萍\n",
       "4   e386e027-8299-4703-a748-8ab7d060348c      曾雯燕\n",
       "5   6d3cb16e-239d-495a-a3cc-b6f346b07034      谢依希\n",
       "6   81ec76dd-163f-4ee3-aa16-11a0ce73295e      杨悦聪\n",
       "7   6c940c74-645a-45c8-884d-6ab34ba352c2       周雨\n",
       "8   d8c64294-0629-4e81-87d5-3976ad54ac0e      刘瑜鹏\n",
       "9   c0ee4410-bca0-4a3f-acb5-74c8eb72f0a1      陈嘉仪\n",
       "10  4278a690-5f78-40d7-abe5-88eab5d5494f      徐旖芊\n",
       "11  e61d920f-f131-464a-9a43-a82babf20358      刘心如\n",
       "12  a968313c-ab4e-42d0-9265-749dcf9b8529       刘宇\n",
       "13  8b1c2538-05cc-4636-8460-cfa8059d5c72       李婷\n",
       "14  d0c25191-8224-410e-9eed-df3457e0a77f      黄智毅\n",
       "15  454e8382-9ee5-4264-b97f-02ecbe5bd987      黄慧文\n",
       "16  5bfad450-0274-40b5-ba9f-0e4a0af9763b      张铭睿\n",
       "17  a9c9451c-8627-4eed-b587-9410d5a61ff9       陈婷\n",
       "18  6a663a8e-725c-4c7d-bd72-5520c4a2e93e      洪可凡\n",
       "19  050bf519-7cdf-4b6a-8aac-52f475d282f8      卢继志\n",
       "20  0523267b-6750-4d1e-987d-674e0ecb0821      张梓乐"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#facelist里面的数据\n",
    "faceListId_df = pd.json_normalize(r_get_facelist.json()[\"persistedFaces\"])# 升级pandas才能运行\n",
    "faceListId_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>persistedFaceId</th>\n",
       "      <th>confidence</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>0.29269</td>\n",
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       "      <th>1</th>\n",
       "      <td>8b1c2538-05cc-4636-8460-cfa8059d5c72</td>\n",
       "      <td>0.20908</td>\n",
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       "      <td>0.17849</td>\n",
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       "      <td>0.16209</td>\n",
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       "      <td>6a663a8e-725c-4c7d-bd72-5520c4a2e93e</td>\n",
       "      <td>0.10100</td>\n",
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       "      <td>0.09990</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5081698a-efe4-4be9-822c-cb58f2cc4803</td>\n",
       "      <td>0.09955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>d0c25191-8224-410e-9eed-df3457e0a77f</td>\n",
       "      <td>0.09503</td>\n",
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       "                        persistedFaceId  confidence\n",
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       "3  c0ee4410-bca0-4a3f-acb5-74c8eb72f0a1     0.16209\n",
       "4  e61d920f-f131-464a-9a43-a82babf20358     0.15023\n",
       "5  6a663a8e-725c-4c7d-bd72-5520c4a2e93e     0.10100\n",
       "6  8a559e64-a44b-4a4b-84dc-e14da959da3a     0.10034\n",
       "7  0523267b-6750-4d1e-987d-674e0ecb0821     0.09990\n",
       "8  5081698a-efe4-4be9-822c-cb58f2cc4803     0.09955\n",
       "9  d0c25191-8224-410e-9eed-df3457e0a77f     0.09503"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 返回相似度的数据\n",
    "find_df = pd.json_normalize(r_findsimilars.json())# 升级pandas才能运行\n",
    "find_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>4278a690-5f78-40d7-abe5-88eab5d5494f</td>\n",
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       "      <td>8b1c2538-05cc-4636-8460-cfa8059d5c72</td>\n",
       "      <td>李婷</td>\n",
       "      <td>0.20908</td>\n",
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       "      <th>7</th>\n",
       "      <td>5bfad450-0274-40b5-ba9f-0e4a0af9763b</td>\n",
       "      <td>张铭睿</td>\n",
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       "      <th>2</th>\n",
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       "      <td>陈嘉仪</td>\n",
       "      <td>0.16209</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e61d920f-f131-464a-9a43-a82babf20358</td>\n",
       "      <td>刘心如</td>\n",
       "      <td>0.15023</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6a663a8e-725c-4c7d-bd72-5520c4a2e93e</td>\n",
       "      <td>洪可凡</td>\n",
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       "      <th>1</th>\n",
       "      <td>8a559e64-a44b-4a4b-84dc-e14da959da3a</td>\n",
       "      <td>林嘉茵</td>\n",
       "      <td>0.10034</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0523267b-6750-4d1e-987d-674e0ecb0821</td>\n",
       "      <td>张梓乐</td>\n",
       "      <td>0.09990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5081698a-efe4-4be9-822c-cb58f2cc4803</td>\n",
       "      <td>丘某峰</td>\n",
       "      <td>0.09955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>d0c25191-8224-410e-9eed-df3457e0a77f</td>\n",
       "      <td>黄智毅</td>\n",
       "      <td>0.09503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        persistedFaceId userData  confidence\n",
       "3  4278a690-5f78-40d7-abe5-88eab5d5494f      徐旖芊     0.29269\n",
       "5  8b1c2538-05cc-4636-8460-cfa8059d5c72       李婷     0.20908\n",
       "7  5bfad450-0274-40b5-ba9f-0e4a0af9763b      张铭睿     0.17849\n",
       "2  c0ee4410-bca0-4a3f-acb5-74c8eb72f0a1      陈嘉仪     0.16209\n",
       "4  e61d920f-f131-464a-9a43-a82babf20358      刘心如     0.15023\n",
       "8  6a663a8e-725c-4c7d-bd72-5520c4a2e93e      洪可凡     0.10100\n",
       "1  8a559e64-a44b-4a4b-84dc-e14da959da3a      林嘉茵     0.10034\n",
       "9  0523267b-6750-4d1e-987d-674e0ecb0821      张梓乐     0.09990\n",
       "0  5081698a-efe4-4be9-822c-cb58f2cc4803      丘某峰     0.09955\n",
       "6  d0c25191-8224-410e-9eed-df3457e0a77f      黄智毅     0.09503"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(faceListId_df, find_df,how='inner', on='persistedFaceId').sort_values(by=\"confidence\",ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 设计人脸识别门禁/打卡/签到 小程序"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face++ FaceSets 实践\n",
    "\n",
    "\n",
    ">* 1. FaceSet Create\n",
    ">* 2. FaceSet GetDetail\n",
    ">* 3. FaceSet AddFace\n",
    ">* 4. FaceSet RemoveFace\n",
    ">* 5. FaceSet Update\n",
    ">* 6. Compare Face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_secret = \"0TJgn-DlRWEY7s3-ZwqpwExpAt_AiPgk\"\n",
    "api_key = \"J5pFN6-H9er4suRIycwOes3CTUlwMBZY\"  # Replace with a valid Subscription Key here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet Create（创建人脸集合）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. FaceSet Create\n",
    "import requests,json\n",
    "\n",
    "display_name = \"网新一班人脸集合\"\n",
    "outer_id = \"00001\"\n",
    "user_data = \"52人，20男生，32女生\"\n",
    "\n",
    "CreateFace_Url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/create\"\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'display_name':display_name,\n",
    "    'outer_id':outer_id,\n",
    "    'user_data':user_data\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(CreateFace_Url, params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time_used': 71,\n",
       " 'error_message': 'AUTHORIZATION_ERROR: Insufficient Account Balance',\n",
       " 'request_id': '1603530044,fcba1dd4-dd41-4e27-8bb1-988113a593f2'}"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet GetDetail（获取人脸集合信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "GetDetail_Url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/getdetail\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'outer_id':outer_id,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(GetDetail_Url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time_used': 65,\n",
       " 'error_message': 'AUTHORIZATION_ERROR: Insufficient Account Balance',\n",
       " 'request_id': '1603530090,a66a2436-f428-4316-8303-c8f89a80efc0'}"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet AddFace（增加人脸信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "AddFace_url = \" https://api-cn.faceplusplus.com/facepp/v3/faceset/addface\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'37071d95016c1b2d81591a6f0c1681f2',\n",
    "    'face_tokens':'b0407b9e803ebd39d511cd7956fd5bf5',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(AddFace_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time_used': 74,\n",
       " 'error_message': 'AUTHORIZATION_ERROR: Insufficient Account Balance',\n",
       " 'request_id': '1603530238,2f27345e-567a-4233-86fa-c57ed52d4501'}"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet RemoveFace（移除人脸信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "RemoveFace_url = \" https://api-cn.faceplusplus.com/facepp/v3/faceset/removeface\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'37071d95016c1b2d81591a6f0c1681f2',\n",
    "    'face_tokens':'b0407b9e803ebd39d511cd7956fd5bf5',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(RemoveFace_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time_used': 61,\n",
       " 'error_message': 'AUTHORIZATION_ERROR: Insufficient Account Balance',\n",
       " 'request_id': '1603530250,75fb89d0-87a9-4a3b-8209-9a863bd9e85e'}"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet Update（更新人脸集合信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "Update_url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/update\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'37071d95016c1b2d81591a6f0c1681f2',\n",
    "    'user_data':\"53人，21男生，32女生\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Update_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time_used': 74,\n",
       " 'error_message': 'AUTHORIZATION_ERROR: Insufficient Account Balance',\n",
       " 'request_id': '1603530257,f6a82bb5-c1a1-412f-b4f5-19112b323e60'}"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Face（对比人脸相似度）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "liudehua01 = \"https://gss0.baidu.com/9fo3dSag_xI4khGko9WTAnF6hhy/zhidao/pic/item/7c1ed21b0ef41bd57f7f20ff57da81cb39db3d89.jpg\"\n",
    "liudehua02 = \"https://tse3-mm.cn.bing.net/th/id/OIP.Xz3HbYZeNrdUnGJ7vXNzsQHaKO?pid=Api&rs=1\"\n",
    "wangzulan = \"https://tse3-mm.cn.bing.net/th/id/OIP.ZnXeGoVYT4jQudiPOGZn3QAAAA?pid=Api&rs=1\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方案1:直接对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "Compare_url = \"https://api-cn.faceplusplus.com/facepp/v3/compare\"\n",
    "\n",
    "payload ={\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'image_url1':liudehua01,\n",
    "    'image_url2':wangzulan\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Compare_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time_used': 75,\n",
       " 'error_message': 'AUTHORIZATION_ERROR: Insufficient Account Balance',\n",
       " 'request_id': '1603530276,c3024374-342a-4281-8cef-ab24a340d71f'}"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方案2:与人脸集合进行对比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 面部检测(获取face_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "Detect_url = 'https://api-cn.faceplusplus.com/facepp/v3/detect' \n",
    "img_url = liudehua01\n",
    "\n",
    "payload = {\n",
    "    \"image_url\":img_url,\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'return_attributes':'gender,age,smiling,emotion', \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Detect_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'request_id': '1603530283,e475d887-5fb0-4034-b58c-190ada53a228',\n",
       " 'time_used': 78,\n",
       " 'error_message': 'AUTHORIZATION_ERROR: Insufficient Account Balance'}"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 加入人脸集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸识别社会/政治使用思考\n",
    "> 人脸识别的 API比较\n",
    ">> * [Face Off: Confronting Bias in Face Recognition AI](https://www.kairos.com/blog/face-off-confronting-bias-in-face-recognition-ai)\n",
    ">> * [为什么世界现在需要道德的面部识别](https://www.kairos.com/blog/why-the-world-needs-ethical-facial-recognition-now)\n",
    "    * 到2023年，全球人脸识别市场的价值估计接近100亿美元，复合年增长率为16.8％。市场背后的主要增长动力是监视市场的发展以及生物识别技术领域的政府支出。但是，面部识别的使用在其他领域也有很大贡献，例如帮助企业打击消费者欺诈，满足动态法规遵从性以及提供可获利的客户体验。\n",
    "\n",
    "-----\n",
    "> 人脸识别的偏差及API 政治经济学 \n",
    ">> * [对抗：面对面部识别AI中的偏见](https://www.kairos.com/blog/face-off-confronting-bias-in-face-recognition-ai)\n",
    "    * 发生了什么？\n",
    "        * “编码注视”或算法偏差的研究: 浅肤色男性的错误率为0.8％ ?  深色皮肤女性的错误率高达34.7％  ? (Microsoft，IBM和Face ++)\n",
    "    * 用户的反应范围从惊奇和赞美到不悦和冒犯\n",
    "        * 我们承认目前提供的种族分类（黑人，白人，亚裔，西班牙裔，其他则不足以代表丰富多样，发展迅速的文化和种族挂毯\n",
    "    * 该怎么办？\n",
    "        * 改善数据\n",
    "        * 寻求持续的反馈    \n",
    "        \n",
    ">>  * [科技行业没有应对面部识别偏见的计划](https://www.theverge.com/2018/7/26/17616290/facial-recognition-ai-bias-benchmark-test) \n",
    ">>> 公司在做什么？(一些高科技大公司支持基准和法规)     \n",
    ">>> 我们如何解决偏见问题？    \n",
    ">>> 偏差不是唯一的问题\n",
    "\n",
    ">> * [面部识别尚未准备好给执法部门使用](https://techcrunch.com/2018/06/25/facial-recognition-software-is-not-ready-for-use-by-law-enforcement/)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 计算机视觉\n",
    "\n",
    "* 学习并完成所有Azure computer version 的API调用\n",
    "> * 分析远程图像    \n",
    "> * 分析本地图片    \n",
    "> * 生成缩略图    \n",
    "> * 提取印刷体文本和手写文本    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析远程图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"categories\": [{\"name\": \"people_portrait\", \"score\": 0.63671875, \"detail\": {\"celebrities\": [{\"name\": \"Yuan Wang\", \"confidence\": 0.9981421232223511, \"faceRectangle\": {\"left\": 140, \"top\": 296, \"width\": 556, \"height\": 556}}]}}], \"color\": {\"dominantColorForeground\": \"White\", \"dominantColorBackground\": \"White\", \"dominantColors\": [\"White\", \"Black\", \"Grey\"], \"accentColor\": \"80504B\", \"isBwImg\": false, \"isBWImg\": false}, \"description\": {\"tags\": [\"necktie\", \"person\", \"wall\", \"wearing\", \"indoor\", \"clothing\", \"shirt\", \"young\", \"dress\", \"boy\", \"white\", \"posing\", \"bow\", \"dressed\", \"hair\", \"staring\"], \"captions\": [{\"text\": \"a young man with a white shirt and a gold tie\", \"confidence\": 0.36088594794273376}]}, \"requestId\": \"dd9080bd-05f3-4382-a87e-45007f7be8e5\", \"metadata\": {\"height\": 1536, \"width\": 1024, \"format\": \"Jpeg\"}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "endpoint = \"https://apiwy.cognitiveservices.azure.com/\"\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "subscription_key = \"8a58412de1f5433f8fe24c5b53257825\"\n",
    "\n",
    "# base url\n",
    "analyze_url = endpoint+ \"vision/v3.1/analyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"http://img.mp.itc.cn/upload/20170520/a0b0c6c6f52343d2af23689ddca17f66_th.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "# 参数\n",
    "params = {'visualFeatures': 'Categories,Description,Color'}\n",
    "# 请求主体body\n",
    "data = {'url': image_url}\n",
    "response = requests.post(analyze_url, headers=headers,\n",
    "                         params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The most\n",
    "# relevant caption for the image is obtained from the 'description' property.\n",
    "analysis = response.json()\n",
    "print(json.dumps(response.json()))\n",
    "image_caption = analysis[\"description\"][\"captions\"][0][\"text\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the caption.\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(image_caption, size=\"x-large\", y=-0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析本地图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'categories': [{'name': 'sky_cloud', 'score': 0.390625}], 'color': {'dominantColorForeground': 'Black', 'dominantColorBackground': 'Black', 'dominantColors': ['Black'], 'accentColor': '894289', 'isBwImg': False, 'isBWImg': False}, 'description': {'tags': ['blur'], 'captions': [{'text': 'a flower floating in water', 'confidence': 0.49404221773147583}]}, 'requestId': '0161ea31-3161-4f38-9223-3d9ed4ee3af4', 'metadata': {'height': 913, 'width': 1623, 'format': 'Jpeg'}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "\n",
    "# analyze_url = endpoint + \"vision/v2.1/analyze\"\n",
    "\n",
    "# Set image_path to the local path of an image that you want to analyze.\n",
    "image_path = \"hua.jpg\" \n",
    "# Read the image into a byte array\n",
    "image_data = open(image_path,\"rb\").read()\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"8a58412de1f5433f8fe24c5b53257825\",\n",
    "           'Content-Type': 'application/octet-stream'}\n",
    "params = {'visualFeatures': 'Categories,Description,Color'}\n",
    "response = requests.post(\n",
    "    analyze_url, headers=headers, params=params, data=image_data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The most\n",
    "# relevant caption for the image is obtained from the 'description' property.\n",
    "analysis = response.json()\n",
    "print(analysis)\n",
    "image_caption = analysis[\"description\"][\"captions\"][0][\"text\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the caption.\n",
    "image = Image.open(BytesIO(image_data))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(image_caption, size=\"x-large\", y=-0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成缩略图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thumbnail is 100-by-100\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "\n",
    "thumbnail_url = \"https://apiwy.cognitiveservices.azure.com/\" + \"vision/v3.1/generateThumbnail\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"http://img.mp.itc.cn/upload/20170520/a0b0c6c6f52343d2af23689ddca17f66_th.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"dd748cf10bf9404399e5416d9399e218\"}\n",
    "params = {'width': '100', 'height': '100', 'smartCropping': 'true'}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(thumbnail_url, headers=headers,\n",
    "                         params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "thumbnail = Image.open(BytesIO(response.content))\n",
    "\n",
    "# Display the thumbnail.\n",
    "plt.imshow(thumbnail)\n",
    "plt.axis(\"off\")\n",
    "\n",
    "# Verify the thumbnail size.\n",
    "print(\"Thumbnail is {0}-by-{1}\".format(*thumbnail.size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取印刷体文本和手写文本 \n",
    "* 假如我们抓取了1000张网页，出了文本信息我们分析以外，还有每个页面的图片的信息，我们可以用提取图片文本的方式，将图片的信息也抓取下来\n",
    "* 我们进抓取了图片，想知道这些图片的内容是什么，也可以用提取文本的方式进行提取\n",
    "* ...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学习人脸识别和计算机视觉心得体会\n",
    "\n",
    "> 历程艰辛！    \n",
    "> 不会放弃！    \n",
    "> 终得成果！    \n",
    "> 分享经验吧～"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "243px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
